CASE STUDY
most importantly , can be adopted by business users with limited technical support .
How has the solution improved data management capabilities for DNB ?
What business challenges were you looking to address ?
Digitalisation is fuelling an explosive growth in data , bringing with it new challenges and opportunities . For DNB , the imperative is – and will always be – to stay relevant to customers in their daily digital lives . We recognised very early on that the key to us achieving this goal was data and advanced analytics . By capitalising on data insights , DNB can continually enhance the customer digital experience and the value delivered through its customer channels .
We also saw an opportunity to optimise delivery lead times and to create reusable data assets . Data scientists were spending 80 % of their time on data preparation tasks and 20 % on the real value add of building predictive models . We needed a practical solution that would allow us to switch from the 80:20 to 20:80 resource focus . This was where the Denodo platform came into play .
The Denodo solution allowed us to rapidly build reusable data assets that could be easily accessed ( in a controlled way ) and consumed in exploratory analytics use cases . It also fulfilled our requirements around real-time operations , auditing and maximising the utility of data assets and associated resources .
From a data management perspective , Denodo has enabled our data analytics users to browse and harvest value across DNB ’ s complex data ecosystem . Denodo forms the foundation for democratising our data assets .
DNB manages a broad range of data sources . A critical source is the data lake in AWS that stores all DNB transactional data ( over 9B + transactions ). We also have multiple on-premises data warehouses and a Cloudera installation for the real-time management of streaming data entering our banking systems .
The Denodo data virtualisation platform sits on top of all these data sources , to create a DNB data marketplace . The solution provides a single point of controlled access to over 4K + enterprise data warehouse table views , 9B + customer transactions and digital click stream data from our digital channels .
How important is having a robust data strategy for satisfying end-user demand ?
From my experience , it is of critical importance . Having an ambition , underpinned by a well-defined data strategy and a plan for execution , is an imperative . Setting the ambition level and a plan on how to get aligned with key stakeholders across the
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